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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does disclose the read-only return contents (numeric-flag columns, row count, provenance banner), which is the main behavioral trait for this tool. However it says nothing about output format, whether a row limit applies, or error behavior on an unavailable dataset.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with the returned payload front-loaded and the usage directive last. Every clause carries information; nothing is redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must convey the return shape, and it enumerates the four things returned, which is sufficient for an agent to decide to call it first. Minor gaps remain about format and sizing of the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there are no parameter semantics to explain and the baseline of 4 applies. The description correctly does not waste words on inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the exact payload returned (columns, which are numeric, row count, provenance banner) for a named dataset, so the agent knows precisely what resource this reports on. It does not explicitly contrast itself with siblings like dataset_stats or dataset_provenance, which overlap somewhat, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

"Call this first to learn the schema" gives a clear usage directive and ordering relative to the other dataset_* tools. It does not state when not to use it or which sibling supersedes it for narrower questions, so no explicit alternatives are covered.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It discloses one useful trait — results are returned 'in the order given' rather than sorted — but says nothing about read-only safety, result-set size, pagination, or what happens when fewer than two values are supplied (schema enforces 2-10). Too much is left unstated for a zero-annotation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single compact sentence with the resource and query semantics front-loaded and the use case appended after an em-dash. Nothing is padded, though the sentence is fragmentary and would read slightly better with an explicit verb.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 2-parameter read tool with no annotations and no output schema, the description covers purpose and query semantics but leaves the response shape and matching rule implicit. It is minimally adequate but does not fully close the gap left by the missing structured metadata.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It conveys that 'column' is the field to filter on and 'values' are the target values, with an implied equality/any-of match and order preservation, which is more than the bare schema gives. But it omits the 2-10 item bound and what 'any of the given values' returns when the column value is missing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource (rows of the Sauna Cold Plunge Compare dataset) and the operation (return rows matching a column's values, order preserved), so the agent knows it is a filtered multi-row lookup. It is a noun phrase rather than an explicit verb, but the intent and the 'X vs Y' framing make it clear enough. It hints at distinction from siblings like dataset_row (single row) without naming them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The trailing 'for "X vs Y" questions' clause gives implied usage context, pointing to comparison queries specifically. However, no alternatives are named (e.g., dataset_search, dataset_row, dataset_top) and there are no when-not conditions, so the agent must infer when this beats the other row-retrieval tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It signals a pure read ('Read this') over a zero-parameter metadata resource, which is effectively transparent about the safety profile, but it says nothing about failure modes or whether the metadata is stable/cached. For a trivial metadata read this is sufficient, but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, front-loaded with the returned fields, with the usage note last. No filler, no restatement of the tool name or title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description must convey the return shape, and it does so by listing source, date, licence, and citation. That is close to complete for a zero-arg metadata tool; only the exact field names and format remain unspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline is 4. The description appropriately does not invent parameter detail, instead describing the returned content, which is the only semantic surface available.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description enumerates exactly what the tool returns (source, compute date, licence, citation) and names the specific dataset it belongs to, so an agent can distinguish it from dataset_columns, dataset_stats, and the other dataset_* read tools. It lacks a verb, but the noun-level specificity is enough to identify the resource unambiguously.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

"Read this to attribute a figure correctly" gives an implied use case (citation/attribution), which is more than nothing. But it offers no explicit when-not guidance and never names a sibling as the alternative for other metadata needs. Adequate but thin.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden; it usefully discloses that matching is exact and case-insensitive, which is real behavioral content. However, it says nothing about how many rows come back, ordering, or what happens on a miss, leaving key operational behavior undisclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single tight sentence with no filler. It front-loads the dataset name rather than the action, which is a minor ordering quibble, but nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter lookup with no output schema and no annotations, the description should address result cardinality and the empty-result case; it does not. It is adequate for the basic call but leaves an agent unsure what a successful response looks like.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description has to compensate, and it does: 'column' is the column to test and 'value' is what it must equal, with matching exact and case-insensitive. That covers the semantics of both required parameters, though it does not say whether 'column' accepts display names or internal keys.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('the rows ... where a column equals a value') and pins down exact-match semantics, so an agent knows this is a lookup rather than an aggregate or comparison. It stops short of naming which sibling to prefer (e.g. dataset_search for non-exact matching), so sibling differentiation is only implicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Exact equality with case-insensitivity is stated, which implicitly signals when this tool applies versus a fuzzy search, but no alternatives are named and no when-not-to-use guidance is given. Usage must be inferred from the exact-match phrasing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It does disclose the case-insensitive substring-match semantics and the 50-row cap, which are genuinely useful, but it omits return shape, ordering, default limit behavior, and any permission/read-access context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single tight sentence with the resource and matching scope front-loaded. No filler and nothing redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter read tool with no output schema and no annotations, the description covers what is searched and the result cap but leaves ordering, default limit, and return field structure unstated. Adequate but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 50% (the limit parameter has no schema description), yet the description clarifies the matching semantics ('cells contain the query', case-insensitive) and restates the effective result cap of 50, adding meaning beyond the schema for the query parameter. It still does not explain the limit parameter name or its default.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb (search) and resource (rows of the Sauna Cold Plunge Compare dataset), plus the matching rule (case-insensitive, any cell) and result cap (up to 50). It is clear what the tool does, though it does not name or contrast with the sibling tools like dataset_row or dataset_top.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this versus dataset_row, dataset_top, or dataset_compare. Usage is only implied by the verb 'search', and no exclusions or prerequisites are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden, and it does add non-obvious value: grouping commas and currency symbols are parsed, and non-numeric rows are excluded yet counted. However, it omits what happens if the column is entirely non-numeric, whether errors are raised, and how the excluded-row count is surfaced in a response with no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence that front-loads the returned metrics and tucks the data-handling caveats into a parenthetical. No filler, though the parenthetical packs three distinct behaviors into one clause, slightly blurring their priority.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool with no annotations and no output schema, the description covers the essential behavior but leaves gaps: the shape of the returned statistics object, whether the excluded-non-numeric count is included in the result, and error behavior on a non-numeric column. Adequate but not fully self-sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for the single required 'column' parameter, so the description must compensate. It does say the value must be a numeric column of the specific dataset, which constrains the input meaningfully, but it gives no naming format, no hint that a sibling (dataset_columns) enumerates valid column names, and no example.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific operation (summary statistics) on a specific resource (a numeric column of the Sauna Cold Plunge Compare dataset) and even enumerates the returned metrics (count, min, max, mean, median, sum). It is clearly distinguishable in substance from siblings like dataset_top or dataset_search, though it never explicitly contrasts itself with them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: 'a numeric column' tells the agent the tool applies to numeric columns, and the handling notes suggest it tolerates messy formatting. There is no explicit when-to-use vs. when-not, no mention of alternatives such as dataset_top for ranking, and no prerequisites stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden. It does convey the core semantics (ranking direction is selectable, the column must be numeric), and read-only intent is implicit in 'rows of ... dataset'. However, it says nothing about tie-breaking, the default of top-N when limit is omitted, or the maximum of 50 already implied by the schema, so behavioral disclosure is only partial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence that states the operation, the resource, and the ordering key, plus a short natural-language trigger example. No filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema and no annotations, so the description is the only source of behavioral context. It adequately conveys what the tool returns conceptually (rows), but omits the return shape, default limit behavior, and tie handling — gaps an agent would need to fill by trial.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 33% — only 'ascending' is documented. The description usefully adds that the column must be numeric, a constraint the schema does not express (it only requires a non-empty string). It says nothing about the 'limit' parameter or the default ordering, so it only partially compensates for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific operation (returning the highest/lowest rows) on a named resource (the Sauna Cold Plunge Compare dataset) with a clear ordering key (a numeric column). It does not explicitly contrast itself with siblings like dataset_stats or dataset_row, so it falls short of 5, but an agent can grasp the intent immediately.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The quoted example 'which is the most/least X' implies the use case, but there is no explicit when-to-use guidance, no statement of when to prefer dataset_stats or dataset_search instead, and no mention of prerequisites. Usage is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does substantial work: it discloses that nothing is bought, ordered or paid, that no quote is guaranteed, that it is free, and that it returns the recipients, consent wording, and confirmation mechanism. This is meaningful behavioral context beyond a bare read tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences with the imperative 'Read first' front-loaded, followed by the substantive disclosure. Little waste, though the title and description overlap somewhat in restating the non-purchase framing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only metadata tool with no output schema, the description covers what is returned (recipients, consent wording, confirmation flow) and what the tool guarantees or does not. Nothing critical to calling it is missing, though the return shape is described only in prose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so per the rubric the baseline is 4. The description correctly implies there is nothing to pass, though it does not explicitly state that it takes no input.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the specific subject (what submit_enquiry does on Sauna Cold Plunge Compare) and the resource (an enquiry with human providers), and enumerates the metadata it returns. It is slightly indirect since it describes its sibling's behavior rather than itself, but an agent can tell it apart from enquiry_fields and submit_enquiry.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The opening 'Read first' gives explicit ordering guidance — call this before submit_enquiry — and the title/body rule out what this is not (not a purchase, not a guaranteed quote). It stops short of naming alternative siblings or naming the exact call site, so it's clear context without full exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the full behavioral burden. To its credit it discloses the shape of the returned data in detail (key, label, type, required, help text, options), which is the main behavioral fact for a zero-argument getter, but it says nothing about read-only nature, scoping (why specifically the 'Sauna Cold Plunge Compare' enquiry), error behavior, or caching.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, tightly packed, with the field inventory front-loaded and the submit_enquiry routing instruction second. The opening clause is a verbless noun phrase ('Every field of the ... enquiry:') which is slightly clipped, but nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description must explain what comes back, and it does so by enumerating the returned properties. It is nearly sufficient for an agent to call and consume the tool; only the absence of any note on the specific enquiry scope and failure modes keeps it from a 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the schema baseline is 4. The description correctly adds no parameter narrative and instead spends its words on the returned keys, which is the relevant semantic payload here.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource precisely (every field of the Sauna Cold Plunge Compare enquiry) and enumerates exactly what each entry contains: key, label, type, required flag, help text, and allowed options. It is unmistakably distinguishable from dataset_* siblings, though it never states the verb explicitly ('returns'/'lists') and does not contrast itself with enquiry_describe.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a concrete downstream usage rule: 'Pass answers to submit_enquiry keyed by field key,' which tells the agent this tool is the prerequisite discovery step before submit_enquiry. There is no explicit when-not guidance or comparison with enquiry_describe, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and discloses critical behaviors: two-step validation, returned summary and consent line, confirmation token, email verification link required before providers see it, and the exact consent meaning. This is exemplary.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Despite covering a complex two-step flow, every sentence earns its place. The critical 'NOT a purchase' constraint is front-loaded, and steps are clearly labeled without wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-step consent-based enquiry tool with no output schema, the description fully explains inputs, outputs (summary, consent line, token), and post-submission behavior, leaving no gaps an agent would need to infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds workflow context such as 'keyed by field key from enquiry_fields' and the step-1/step-2 role of the confirmation token, going beyond the schema's own descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb+resource ('Submits an enquiry to Sauna Cold Plunge Compare') and immediately disambiguates from a purchase or guaranteed quote, which distinguishes it from sibling tools like dataset_* and enquiry_fields.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit when-to-use instructions via a two-step process: step 1 with answers and consent=true, then step 2 only if the person agrees, and clearly states it is not a purchase. No alternatives are needed given the sibling set.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.